Technology is promising more advantages than ever before. People want things to be cheaper, faster, more convenient and delivered to their doors at no extra cost.

Supermarkets without checkouts; clothes shops that take your measurements in seconds and carry out custom tailoring in minutes; schools with increasing robotization of teachers and hospitals with non-human doctors; autonomous cars; restaurants with mechanized menus; galleries showing art made by artificial intelligence (AI); and live music performances by algorithmic composers are just a few examples of future possibilities. Many of these examples may seem like science fiction – but they are nevertheless already being realized in society.

Automation refers to processes that are performed without human intervention or assistance. With digital technology, the speed and reach of automation is now increasing rapidly. It may already be common in workplaces, but what will happen when all of society is automated? Will a life made up of more automated processes still feel human? And what will our place as individuals be when everything is smarter, more exact and logical?

Automation lends itself to creating an orderly society, but when conflicting yet autonomous processes happen simultaneously, could it also become more chaotic?

The Ericsson 10 Hot Consumer Trends 2019 reveal that people are experiencing mixed emotions. Almost half of the respondents in the survey think that, for better or worse, the internet has replaced many of the simple pleasures of daily life.

As digital technology spreads throughout society, all these hopes and fears simultaneously filter through consumers’ minds. The perspectives are staggering – and consumer views on a near-future automated society are very much the theme of this report.

Organizational culture can accelerate the application of analytics, amplify its power, and steer companies away from risky outcomes. Here are seven principles that underpin a healthy data culture.

Revolutions, it’s been remarked, never go backward. Nor do they advance at a constant rate. Consider the immense transformation unleashed by data analytics. By now, it’s clear the data revolution is changing businesses and industries in profound and unalterable ways.

But the changes are neither uniform nor linear, and companies’ data-analytics efforts are all over the map. McKinsey research suggests that the gap between leaders and laggards in adopting analytics, within and among industry sectors, is growing. We’re seeing the same thing on the ground. Some companies are doing amazing things; some are still struggling with the basics; and some are feeling downright overwhelmed, with executives and members of the rank and file questioning the return on data initiatives.

For leading and lagging companies alike, the emergence of data analytics as an omnipresent reality of modern organizational life means that a healthy data culture is becoming increasingly important. With that in mind, we’ve spent the past few months talking with analytics leaders at companies from a wide range of industries and geographies, drilling down on the organizing principles, motivations, and approaches that undergird their data efforts. We’re struck by themes that recur over and again, including the benefits of data, and the risks; the skepticism from employees before they buy in, and the excitement once they do; the need for flexibility, and the insistence on common frameworks and tools. And, especially: the competitive advantage unleashed by a culture that brings data talent, tools, and decision making together.
The experience of these leaders, and our own, suggests that you can’t import data culture and you can’t impose it. Most of all, you can’t segregate it. You develop a data culture by moving beyond specialists and skunkworks, with the goal of achieving deep business engagement, creating employee pull, and cultivating a sense of purpose, so that data can support your operations instead of the other way around.

In this article, we present seven of the most prominent takeaways from conversations we’ve had with these and other executives who are at the data-culture fore. None of these leaders thinks they’ve got data culture “solved,” nor do they think that there’s a finish line. But they do convey a palpable sense of momentum. When you make progress on data culture, they tell us, you’ll strengthen the nuts and bolts of your analytics enterprise.

That will not only advance your data revolution even further but can also help you avoid the pitfalls that often trip up analytics efforts. We’ve described these at length in another article and have included, with three of the seven takeaways here, short sidebars on related “red flags” whose presence suggests you may be in trouble—along with rapid responses that can mitigate these issues. Taken together, we hope the ideas presented here will inspire you to build a culture that clarifies the purpose, enhances the effectiveness, and increases the speed of your analytics efforts.

New technologies, particularly artificial intelligence, have the potential to propel the rate of learning in business to new heights—the volume and velocity of data have exploded, and algorithms can unlock complex patterns and insights with unprecedented speed. In an era of shrinking product life cycles and rapidly changing business models, the companies that are the first to decode new trends or emerging needs have the best chance to take advantage of them.

But learning at the speed of algorithms requires more than algorithms themselves. New technology can accelerate learning in individual process steps, but to create aggregate organizational learning and competitive advantage it must be complemented by organizational innovation. Moreover, slow-moving contextual shifts, driven by social, political, and economic forces, are becoming just as important to business as fast-moving technologies. To compete on the ability to learn, therefore, leaders must reinvent their organizations to leverage both human and machine capabilities synergistically in order to expand learning to both faster and slower timescales.

A Brief History of Learning Organizations

In first-generation learning organizations, businesses learned how to execute existing processes more efficiently—best exemplified by the “experience curve.” As Bruce Henderson observed half a century ago, firms tend to reduce their costs at a constant and predictable rate as their cumulative experience increases. For example, in the early 20th century costs of the Model T consistently fell by about 25% every time the cumulative product volume doubled.

In this model, learning was a game of continuous improvement aimed at reducing marginal costs. Competing on learning was essentially about building volume, and therefore experience, faster than competitors. This permitted a strategy of pricing for the anticipated value of learning and pursuing cost reductions systematically, using mechanisms such as statistical process control, kaizen, Six Sigma, and quality circles.

In recent years, a second-generation concept of learning came to the forefront: learning how to envision and create new products. In other words, companies must learn not only to descend experience curves but also to “jump” from one curve to another.

This second dimension of learning has always existed in business, but its importance has grown. Technological innovation has compressed product life cycles, so new learning curves appear before old ones have fully played out—and firms must balance both dimensions of learning at the same time. For example, Netflix jumped from a DVD rental business to a streaming service to in-house content creation, while expanding to 190 countries, in less than a decade.

Today, a third phase of the learning game is beginning to unfold. Modern technologies, such as sensors, digital platforms, and AI, promise to massively accelerate the rate at which information is generated, gathered, and processed. This potentially enables companies to operate at superhuman speed, learning about the market and reacting in seconds or even milliseconds.

At the same time, however, companies must also expand their learning abilities to consider longer timescales, as social, political, and economic shifts gradually reshape the business context. Most businesses have woken up to the reality of time compression, but this is only half the picture. The range of timescales that need to be considered is being stretched in both directions. A third-generation learning organization is one that can embrace this new reality—adopting algorithmic principles over shorter timescales while adapting to nonbusiness forces that operate over longer ones.

To make this leap, businesses cannot rely on technological sophistication alone. Repeating a well-established historical pattern, evolution of the organizational model is needed to unlock the potential of new technologies. The original experience curve could be exploited only when new industrial technologies were complemented by organizational innovations like new factory layouts, redefined roles for workers (such as the assembly line), and new managerial approaches like quality circles and kanban. In the same way, to build the third generation of learning organizations, leaders must reinvent the enterprise not only to unlock the potential of new technologies but also to synergistically combine the unique learning capabilities and timescale advantages of both humans and technology—in other words, to build effective “human + machine” machines.

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The third generation of learning organizations presents an enormous opportunity. Companies can unleash both the power of technology for rapid learning and human ingenuity on longer timescales. But this will require leaders first to reimagine the organization and how it is managed.

Companies can determine whether they should invest in blockchain by focusing on specific use cases and their market position.

Speculation on the value of blockchain is rife, with Bitcoin—the first and most infamous application of blockchain—grabbing headlines for its rocketing price and volatility. That the focus of blockchain is wrapped up with Bitcoin is not surprising given that its market value surged from less than $20 billion to more than $200 billion over the course of 2017.1 Yet Bitcoin is only the first application of blockchain technology that has captured the attention of government and industry.

Blockchain was a priority topic at Davos; a World Economic Forum survey suggested that 10 percent of global GDP will be stored on blockchain by 2027.2 Multiple governments have published reports on the potential implications of blockchain, and the past two years alone have seen more than half a million new publications on and 3.7 million Google search results for blockchain.Most tellingly, large investments in blockchain are being made. Venture-capital funding for blockchain start-ups consistently grew and were up to $1 billion in 2017.3 The blockchain-specific investment model of initial coin offerings (ICOs), the sale of cryptocurrency tokens in a new venture, has skyrocketed to $5 billion. Leading technology players are also heavily investing in blockchain: IBM has more than 1,000 staff and $200 million invested in the blockchain-powered Internet of Things (IoT).4

Despite the hype, blockchain is still an immature technology, with a market that is still nascent and a clear recipe for success that has not yet emerged. Unstructured experimentation of blockchain solutions without strategic evaluation of the value at stake or the feasibility of capturing it means that many companies will not see a return on their investments. With this in mind, how can companies determine if there is strategic value in blockchain that justifies major investments?

Our research seeks to answer this question by evaluating not only the strategic importance of blockchain to major industries but also who can capture what type of value through what type of approach. In-depth, industry-by-industry analysis combined with expert and company interviews revealed more than 90 discrete use cases of varying maturity for blockchain across major industries (see interactive in original article).

We evaluated and stress tested the impact and feasibility of each of these use cases to understand better blockchain’s overall strategic value and how to capture it.

Our analysis suggests the following three key insights on the strategic value of blockchain:

Blockchain does not have to be a disintermediator to generate value, a fact that encourages permissioned commercial applications.

Blockchain’s short-term value will be predominantly in reducing cost before creating transformative business models.

Blockchain is still three to five years away from feasibility at scale, primarily because of the difficulty of resolving the “coopetition” paradox to establish common standards.

Companies should take the following structured approach in their blockchain strategies:

Identify value by pragmatically and skeptically assessing impact and feasibility at a granular level and focusing on addressing true pain points with specific use cases within select industries.

Capture value by tailoring strategic approaches to blockchain to their market position, with consideration of measures such as ability to shape the ecosystem, establish standards, and address regulatory barriers.

With the right strategic approach, companies can start extracting value in the short term. Dominant players who can establish their blockchains as the market solutions should make big bets now.

More: /www.mckinsey.com

About the authors: Brant Carson is a partner in McKinsey’s Sydney office, where Giulio Romanelli is an associate partner, and Askhat Zhumaev is a consultant; Patricia Walsh is a consultant in the Melbourne office. The authors wish to thank Dorian Gärtner, Matt Higginson, Jeff Penney, Gregor Theisen, Jen Vu, and Garima Vyas for their contributions to this article.